Jack A. Kosmicki

This study identifies 207 height-associated genes using rare protein-altering variants from over 1.4 million individuals, showing large effect sizes and overlap with Mendelian height syndromes.

This study quantifies cancer risk and penetrance in CHEK2 heterozygotes using genomic data from two large biobanks, finding increased risks for several cancers and younger age of onset.

The paper introduces REMETA, a tool for efficiently performing meta-analysis of gene-based tests using summary statistics, demonstrating its application on UK Biobank data.

The paper improves rare variant association studies by using proteomics-informed large language models, identifying more gene-trait associations than existing methods.

The paper introduces SBAT, a method for joint testing of rare variant burden scores, showing it is well calibrated and outperforms existing tests in some scenarios.

The paper quantifies regional missense mutational intolerance using 730,947 exomes to identify constrained gene regions and improve variant interpretation.

The paper identifies a rare variant that lowers ACE2 expression and reduces COVID-19 risk, and develops a genetic risk score predicting severe disease.

This study uses exome sequencing of 628,388 individuals to identify genetic variants associated with clonal hematopoiesis and its health outcomes.

The paper introduces REGENIE, a fast whole genome regression method for quantitative and binary traits, showing computational efficiency and accuracy on UK Biobank data.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.

The paper estimates the risk of five disorders from paternal-age-related de novo mutations using whole-exome-sequencing data and Danish registry data.

The paper assesses the applicability of the mutation-selection balance model to human protein-truncating variants using simulations and data from exome datasets.

This study identifies 99 genes associated with autism spectrum disorder through large-scale exome sequencing, showing differences in mutation frequencies between ASD and severe neurodevelopmental delay groups.

This study examines the contribution of protein-coding de novo mutations to schizophrenia risk using whole-exome sequencing data from parent-offspring trios, finding a modest but polygenic effect.

The paper identifies de novo variants in neurodevelopmental disorders with epilepsy and finds associations with specific genes and clinical features.

The paper investigates the genetic burden of rare protein-truncating variants in ASD and ADHD, finding similar patterns of enrichment.

The paper identifies 91 genes associated with neurodevelopmental disorders but highlights critical statistical flaws in the analysis that reduce the validity of the findings.

The paper develops a paralog conservation score to identify disease-associated genes and shows that disease variants are enriched at conserved sites across gene families.

Sub-genic missense depletion in ExAC anchors MPC, a deleteriousness score; de novo missense variants with MPC ≥ 2 occur in neurodevelopmental cases at 5.79 times the control rate.

The paper uses polygenic transmission disequilibrium to show common and rare variants act additively in autism risk, with polygenic risk over-transmitted to ASD cases but not unaffected siblings.

The paper shows that many de novo protein truncating variants in neurodevelopmental disorders are common in population cohorts and do not contribute to risk, using data from 9246 families and 60,706 adults.